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10th International Conference on Innovations in Bio-Inspired computing and Applications, IBICA 2019 and 9th World Congress on Information and Communication Technologies, WICT 2019

作者： Rekha, G. Tyagi, Amit Kumar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad India School of Computing Science and Engineering Vellore Institute of Technology Chennai Campus ChennaiTamilnadu600127 India

ISBN: (纸本)9783030493387

In domain of data mining, learning from imbalanced class distribution datasets is a challenging problem for conventional classifiers. The class imbalance exists when the number of samples of one class is much lesser than the ones of the other classes. In real-world classification problems, data samples often have unequal class distribution. This problem is represented as a class imbalance problem. However, many solutions have been proposed in the literature to improve classifier performance. But recent works entitlement that imbalanced dataset is not a problem in itself. The degradation of classifier performance is also linked with many factors like small sample size, sample overlapping, class disjunct and many more. In this work, we proposed cluster-based under-sampling based on farthest neighbors. The majority class samples are selected based on the average distance to all minority class samples in the cluster are farthest. The experimental results show that our cluster-based under-sampling approach outperform with existing techniques in the previous studies. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Classification (of information)

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Learning to augment distributions for out-of-distribution detection 23

Learning to augment distributions for out-of-distribution de...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Qizhou Wang Zhen Fang Yonggang Zhang Feng Liu Yixuan Li Bo Han Department of Computer Science Hong Kong Baptist University Australian Artificial Intelligence Institute University of Technology Sydney School of Computing and Information Systems The University of Melbourne Department of Computer Sciences University of Wisconsin-Madison

Open-world classification systems should discern out-of-distribution (OOD) data whose labels deviate from those of in-distribution (ID) cases, motivating recent studies in OOD detection. Advanced works, despite their promising progress, may still fail in the open world, owing to the lack of knowledge about unseen OOD data in advance. Although one can access auxiliary OOD data (distinct from unseen ones) for model training, it remains to analyze how such auxiliary data will work in the open world. To this end, we delve into such a problem from a learning theory perspective, finding that the distribution discrepancy between the auxiliary and the unseen real OOD data is the key to affecting the open-world detection performance. Accordingly, we propose Distributional-Augmented OOD Learning (DAL), alleviating the OOD distribution discrepancy by crafting an OOD distribution set that contains all distributions in a Wasserstein ball centered on the auxiliary OOD distribution. We justify that the predictor trained over the worst OOD data in the ball can shrink the OOD distribution discrepancy, thus improving the open-world detection performance given only the auxiliary OOD data. We conduct extensive evaluations across representative OOD detection setups, demonstrating the superiority of our DAL over its advanced counterparts. The code is publicly available at: https://***/tmlr-group/DAL.

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A Comprehensive Analysis in Investigating the Impact of Big Data Analytics in Wireless Networks in Failure Prediction, Prevention and Recovery 2

A Comprehensive Analysis in Investigating the Impact of Big ...

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2nd International Conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Surya, S. Rajasekaran, P. Yuriy, Shvets Johar, Siti Sarawati Tripathi, Amrendra Magno Ventayen, Randy Joy Computer Science and Engineering School of Computing Kalasalingam University India Information Technology Dr. N.G.P Institute of Technology India Financial University under the Goverment of Russian Federation Institute of Control of Science Ras Moscow Russia Centre for General Studies and Co-curricular Universiti Tun Hussein Onn Malaysia Department of Social Science Parit Raja Johor Darul Takzim Batu Pahat86400 Malaysia School of Computer Science Upes Dehradun India Pangasinan State University Lingayen Campus Philippines

ISBN: (纸本)9781665437899

The current economic and competitive environment has enabled many businesses and industries to implement digital for enhancing the business operations and support in addressing the overall objectives. Dur to this there is a mounting avalanche information being shared between the individuals, companies and governments, the increase in the usage of mobile devices, internet, social media and other ecommerce services has supported in increasing the volume of data. This trend is expected to grow exponentially and hence there is a requirement to provide wireless network in an effective manner, the increased usage of data and information has resulted in facing various failures, like call drops for mobile users, low bandwidth for internet users etc. Hence, it is highly necessary to implement tools to understand the failure prediction, prevention and recovery on the network. Hence, this study is focused in investigating the impact of big data analysis in protecting the wireless networks from such failures and support in processing large volume of information in an effective manner, the big data analytics can also extract useful data will enable in addressing the concerned parties so as to make critical decisions. In this study, the researcher intends to conduct the survey using closed ended questionnaire from the individuals so as to measure the effectiveness of applying big data for failure prevention, protection and recovery in the wireless network. © 2022 IEEE.

关键词： Wireless networks

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Analysis and Detection of Fake News Using Machine Learning

Analysis and Detection of Fake News Using Machine Learning

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： P Sathish Kumar P. Suthanthiradevi C Arul Stephen B Ebenezer Abishek S. Sivakumar M Mathiyarasu Computer and Communication Engineering Rajalakshmi institute of technology Chennai Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai Electronics and Communication Engineering Vels Institute of science Technology and Advanced studies Chennai Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Electronics and Communication Engineering BIHER Chennai

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The Fake news becomes a more critical issue nowadays, it may reduce the trust of the public about the particular information. This project focuses on the advancement of fake news detection model using machine learning. The main goal of this project is to develop, analyze a model that can effectively classify the news articles based on their Subject or contents. Natural preprocessing Algorithm and features such as Stemming, Tokenization, Stop word and TF-IDF Vectorization are used to train the machine learning model. Then the Machine learning Algorithms likeLogistic Regression and Random Forest are used to classifying the dataset which gives 98% accuracy and 99% of accuracy score. For this Project we have taken a dataset of 22413 Real news and 23481 Fake News from the online resources. This model has a potential to reduce the impact of fake news by grant the promotion of accurate and reliable information in a society.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Tokenization Reliability Internet of Things

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Cloud computing Improved Clustering using Intrusion Detection

Cloud Computing Improved Clustering using Intrusion Detectio...

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2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023

作者： Bansal, Saloni Singh, Amandeep Bijlwan, Sheela Goyal, Bhawna Dogra, Ayush Lepcha, Dawa Chyophel GLA University Department of Electronics and Communication Engineering Mathura India Gulzar Group of Institutions Computer Science and Engineering Punjab Ludhiana India Uttaranchal Institute of Technology Uttaranchal University Computing Sciences Dehradun248007 India University Center for Research and Development Chandigarh University Punjab Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh University Department of Electronics and Communication Engineering Punjab Mohali India

ISBN: (纸本)9798350339369

It is one of the more difficult tasks and calls for highly skilled specialists to deal with the aspects and systems of action of intruders and then unauthorized users who attempt to restrict the data with the computer network. The development of the computer network, the number of intruders, and then the infiltration are all possible effects of incorporating cloud computing. Generally speaking, the updating that comes from properly managed vocations is wasted through its count above the time and it rises before it manifests with the technique. There are many unthinkable state gaps for spotting invasions, and then the numerous communications are what matter most. In order to achieve infiltration through the capture of many capability systems (maximum and less expensive tasking), the usage of advanced detection with the new then the discovering systems is utilized with many than the previous in the cloud. The network warning identification and variation systems based on machine learning, which is a demanding warning identification technology, were separately examined in research and development with research precision. Where there is still room for improvement, the work detailed here elaborates on the forest by choosing its merits and then uses an improved density-based spatial clustering of applications with noise (DBSCAN) through grouping and then the intrusion identification with the cloud environment to remove these flaws. © 2023 IEEE.

关键词： Cloud computing

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Wireless Sensor Networks: Revolutionizing Explosive Detection

Wireless Sensor Networks: Revolutionizing Explosive Detectio...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： P. S Sujith Kumar C. Anand N. Vasuki Ramesh V S. P. Santhoshkumar Department of Computer Science and Engineering Sree Buddha College of Engineering Alappuzha Kerala Department of Information Technology Nandha College of Technology Erode India Department of Computer Science and Engineering Government College of Engineering Erode India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

One of the important technologies for protecting civilian life from terrorist attacks is explosive detection techniques. The global incidence of terrorist activities has been considerably reduced by the development of explosive detection techniques, which has been made possible by ongoing technology improvements. Wireless sensor networks (WSNs) are one of these developments that has become an innovative tool for explosives detection. This work explores recent empirical research on the application of WSNs to explosives detection and includes statistical analysis of the investigations. Wireless sensor network with compatible specialized sensors is required for effective explosive detection. Three main wireless sensor system aspects that are relevant to explosives detection are covered in this research. It first looks at how scalable wireless sensors are in terms of explosives detection technologies. In the second section, it looks at the mobility and communication capabilities of WSNs and sensors in explosives detecting systems. Finally, it highlights potential hazards and weaknesses and addresses the crucial topic of data security inside wireless sensor networks used for explosive detection. The study also suggests incorporating drone assisted detection as a novel way to improve the coverage and effectiveness of explosives detection systems, adding to the continuous advancement of security technologies meant to shield civilian populations from terrorist threats.

关键词： Wireless communication Wireless sensor networks Terrorism Data security Explosives Sensor systems Hazards Threat assessment Sensors Communication system security

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A New Federated Learning Framework Against Gradient Inversion Attacks

arXiv

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arXiv 2024年

作者： Guo, Pengxin Zeng, Shuang Chen, Wenhao Zhang, Xiaodan Ren, Weihong Zhou, Yuyin Qu, Liangqiong School of Computing and Data Science The University of Hong Kong Hong Kong Department of Mathematics The University of Hong Kong Hong Kong College of Computer Science Beijing University of Technology China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering UC Santa Cruz United States

Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. However, recent studies demonstrate that information exchanged during FL is subject to Gradient Inversion Attacks (GIA) and, consequently, a variety of privacy-preserving methods have been integrated into FL to thwart such attacks, such as Secure Multi-party computing (SMC), Homomorphic Encryption (HE), and Differential Privacy (DP). Despite their ability to protect data privacy, these approaches inherently involve substantial privacy-utility trade-offs. By revisiting the key to privacy exposure in FL under GIA, which lies in the frequent sharing of model gradients that contain private data, we take a new perspective by designing a novel privacy preserve FL framework that effectively "breaks the direct connection" between the shared parameters and the local private data to defend against GIA. Specifically, we propose a Hypernetwork Federated Learning (HyperFL) framework that utilizes hypernetworks to generate the parameters of the local model and only the hypernetwork parameters are uploaded to the server for aggregation. Theoretical analyses demonstrate the convergence rate of the proposed HyperFL, while extensive experimental results show the privacy-preserving capability and comparable performance of HyperFL. Code is available at https://***/Pengxin-Guo/HyperFL. © 2024, CC BY.

关键词： Federated learning

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μ-Phone: Accessible Microscope Attachment for Smartphones

μ-Phone: Accessible Microscope Attachment for Smartphones

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Kefan Song Sixuan Wu Ruijia Peng Jason Cobb Alexander T Adams Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA USA School of Interactive Computing Georgia Institute of Technology Atlanta GA USA School of Computer Science Georgia Institute of Technology Atlanta GA USA Renal Medicine Emory University School of Medicine Atlanta GA USA

ISBN: (数字)9798331530143

ISBN: (纸本)9798331530150

Point-of-care (POC) technologies have the potential to greatly improve the clinical lab testing and diagnostics process since they significantly reduce the wait time before getting the results. Microscope, as a very important instrument widely used in clinical testing, is a key component to be adapted to POC scenarios, and there have been various attempts to do so by replacing conventional microscopes with smartphone-attached systems. However, these attempts all utilized components that are either expensive or bulky, which greatly limits the widespread adaptation and utilization of these devices, especially in areas with limited resources. Thus we propose a design and prototype of $\mu$ -Phone, an accessible microscope attachment for smartphones using cheap and easily accessible components to minimize the obstacles to resource availability. The proposed system has a depth of field of 5.25 micrometers, a pixel resolution of 26.3 pixels per micrometer and a spatial resolution of 2.19 micrometers, which makes it powerful enough to capture microscopic images for identification and analysis at a cellular level. With some future developments, this system has great potential to be deployed worldwide for quick and accurate POC analysis of physiological samples.

关键词： Micrometers Optical microscopy Red blood cells Microscopy Ultraviolet sources Prototypes Production Spatial resolution Smart phones Testing

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BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis

arXiv

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arXiv 2022年

作者： Liang, Shuo Wei, Wei Mao, Xian-Ling Wang, Fei He, Zhiyong Laboratory School of Computer Science and Technology Huazhong University of Science and Technology China School of Computer Science and Technology Beijing Institute of Technology China Institute of Computing Technology Chinese Academy of Sciences China Naval University of Engineering China

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that aims to align aspects and corresponding sentiments for aspect-specific sentiment polarity inference. It is challenging because a sentence may contain multiple aspects or complicated (e.g., conditional, coordinating, or adversative) relations. Recently, exploiting dependency syntax information with graph neural networks has been the most popular trend. Despite its success, methods that heavily rely on the dependency tree pose challenges in accurately modeling the alignment of the aspects and their words indicative of sentiment, since the dependency tree may provide noisy signals of unrelated associations (e.g., the "conj" relation between "great" and "dreadful" in Figure 2). In this paper, to alleviate this problem, we propose a Bi-Syntax aware Graph Attention Network (BiSyn-GAT+). Specifically, BiSyn-GAT+ fully exploits the syntax information (e.g., phrase segmentation and hierarchical structure) of the constituent tree of a sentence to model the sentiment-aware context of every single aspect (called intracontext) and the sentiment relations across aspects (called inter-context) for learning. Experiments on four benchmark datasets demonstrate that BiSyn-GAT+ outperforms the state-of-the-art methods consistently. Copyright © 2022, The Authors. All rights reserved.

关键词： Sentiment analysis

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Emergency Vehicle Navigation in Connected Autonomous Systems Using Enhanced Traffic Management System

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IEEE Internet of Things Journal 2025年第12期12卷 20670-20677页

作者： Raja, Gunasekaran Anbalagan, Sudha Gurumoorthy, Sugeerthi Jegathesan, Darshini Girivel, Niveditha Subramanian Sundaram, Varsha Mani Shanmuga Khowaja, Sunder Ali Dev, Kapal Anna University NGNLab Department of Computer Technology MIT Campus Chennai600044 India Vellore Institute of Technology Chennai Centre for Smart Grid Technologies School of Computer Science and Engineering Chennai600127 India Citicorp Services Private Ltd Software Development Chennai600113 India Dublin City University School of Computing Faculty of Engineering and Computing Glasnevin Campus DublinD09 V209 Ireland Munster Technological University Department of Computer Science Bishopstown CorkT12 P928 Ireland University of Johannesburg Department of Institute of Intelligent Systems Johannesburg2006 South Africa Chitkara University Institute of Engineering and Technology Centre for Research Impact Outcome Chitkara University Punjab Rajpura140401 India

Autonomous Vehicle (AV) usage has become predominant in the rapidly evolving landscape of urban transportation. Integrating AVs and non-AVs in the existing traffic infrastructure has significantly increased the complexity of traffic patterns. This research work primes the enhanced Traffic Management System (e-TMS), a solution implemented to expedite Emergency Vehicle (EV) travel in the context of Connected AVs (CAVs) and ensured secured communication employing Public Key Infrastructure (PKI) among the Internet of Vehicles (IoV) framework. When an EV is detected, the IoV system verifies the EV's signal using PKI, ensuring its authenticity and integrity by encrypting the communication between the EV and Road Side Units (RSUs). Further, the e-TMS activates the platooning process where CAVs in the EV's lane shift to the adjacent lanes and dynamically form platoons to create a dedicated lane for the EV. To further optimize the platooning process, an Adaptive Smart Leader Selection (ASLS) algorithm is employed to select a leader vehicle spontaneously among the AVs based on the proximity to the EV, communication reliability, and lane position. Radio Detection and Ranging devices aid this platooning process by providing distance and target velocity, which are needed to maintain the required distance between the AVs and ensure safe platoon formation. The e-TMS enhances EV response times and overall traffic flow efficiency and resulted in 27.8% increase in mean speed compared to the traditional methods. © 2014 IEEE.

关键词： Emergency vehicles

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